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Studying the Explanatory Capacity of Artificial Neural Networks for Understanding Environmental Chemical Quantitative Structure−Activity Relationship Models

https://doi.org/10.1021/ci050079x
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9783527613106openalex-snapshot
Handbook of Molecular Descriptors
inferredcites →https://doi.org/10.1016/s0039-9140(01)00537-9openalex-snapshot
Non-linear QSAR modeling by using multilayer perceptron feedforward neural networks trained by back-propagation
inferredcites →https://doi.org/10.1016/s0045-6535(97)00178-1openalex-snapshot
Predicting the toxicity of complex mixtures using artificial neural networks
inferredcites →https://doi.org/10.1017/cbo9780511812651openalex-snapshot
Pattern Recognition and Neural Networks
inferredcites →https://doi.org/10.1021/cr950202ropenalex-snapshot
Quantum-Chemical Descriptors in QSAR/QSPR Studies
inferredcites →https://doi.org/10.1061/(asce)0887-3801(2000)14:1(1)openalex-snapshot
River Stage Forecasting in Bangladesh: Neural Network Approach
inferredcites →https://doi.org/10.1093/oso/9780198538493.001.0001openalex-snapshot
Neural Networks for Pattern Recognition
inferredcites →https://doi.org/10.5860/choice.31-5500openalex-snapshot
Neural networks for pattern recognition

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/0471266949.bmc001.pub2openalex-snapshot
History of Quantitative Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1002/cjoc.200790138openalex-snapshot
Quantitative Structure Property Relations (QSPR) for Predicting Molar Diamagnetic Susceptibilities, χm, of Inorganic Compounds
inferred← citeshttps://doi.org/10.1002/inf2.12094openalex-snapshot
Machine learning: Accelerating materials development for energy storage and conversion
inferred← citeshttps://doi.org/10.1002/minf.201501008openalex-snapshot
Deep Learning in Drug Discovery
inferred← citeshttps://doi.org/10.1002/qsar.200960074openalex-snapshot
Is Feature Selection Essential for ANN Modeling?
inferred← citeshttps://doi.org/10.1007/s10822-006-9098-3openalex-snapshot
Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach
inferred← citeshttps://doi.org/10.1016/j.jelectrocard.2006.12.011openalex-snapshot
Best leads in the standard electrocardiogram for the emergency detection of acute coronary syndrome
inferred← citeshttps://doi.org/10.1016/j.jmat.2017.08.002openalex-snapshot
Materials discovery and design using machine learning
inferred← citeshttps://doi.org/10.1021/ci0504014openalex-snapshot
logD7.4 Modeling Using Bayesian Regularized Neural Networks. Assessment and Correction of the Errors of Prediction
inferred← citeshttps://doi.org/10.1038/s41529-022-00218-4openalex-snapshot
Reviewing machine learning of corrosion prediction in a data-oriented perspective
inferred← citeshttps://doi.org/10.1186/s43043-021-00091-2openalex-snapshot
Using artificial intelligence to predict the intrauterine insemination success rate among infertile couples
inferred← citeshttps://doi.org/10.31661/jbpe.v0i0.2201-1449openalex-snapshot
A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests
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